北方石质山区坡面土壤厚度分布特征——以北京市密云县为例

土壤厚度是土壤退化及土地生产力水平评价的一个重要指标。本研究应用插钎法,调查了北京市密云县山区50个基本抽样单元的坡面土壤厚度。对样点数据进行了统计分析,结果表明:平均土壤厚度为15cm。小于10cm的土壤占37%,小于20cm的土壤占76%,小于30cm的占90%。空间上土壤较厚的区域主要集中在西北和东北部植被覆盖较好的地区。对影响土壤厚度的因子进行分析发现:土壤厚度与海拔无明显相关性,而坡向对土壤厚度的影响较为明显,阴坡土壤厚度明显大于阳坡。土壤厚度与植被覆盖度和坡度明显相关,与植被覆盖度的相关系数为0.84,与坡度的相关系数为-0.40,二者都通过了显著水平为0.01的显著性检验。     

GIS支持下三峡库区秭归县滑坡灾害空间预测

基于GIS空间分析和统计模型相结合进行区域评价与空间预测是滑坡灾害研究的重要方向之一。以三峡库区秭归县为研究区,选择坡度、坡向、边坡结构、工程岩组、排水系统、土地利用和公路开挖作为评价因子。为提高模型的预测精度、可信度和推广能力,利用窗口采样规则降低训练样本之间的空间相关性。建立Logistic回归模型,对滑坡灾害与评价因子进行定量相关性分析。计算研究区滑坡灾害易发性指数,对其进行聚类分析,绘制滑坡易发性分区图,其中高、中易发区占整个研究区面积的38.9%,主要分布在人类工程活动频繁和靠近排水系统的区域。经过验证,该模型的预测精度达到77.57%。     

Soil erosion risk evaluation using GIS in the Yuanmou County, a dry-hot valley of Yunnan, China

Soil erosion is a major threat to sustainable agriculture. Evaluating regional erosion risk is increasingly needed by national and in-ternational environmental agencies. This study elaborates a model (using spatial principal component analysis [SPCA]) method for the evaluation of soil erosion risk in a representative area of dry-hot valley (Yuanmou County) at a scale of 1:100,000 using a spatial database and GIS. The model contains seven factors: elevation, slope, annual precipitation, land use, vegetation, soil, and population density. The evaluation results show that five grades of soil erosion risk: very low, low, medium, high, and very high. These are divided in the study area, and a soil erosion risk evaluation map is created. The model may be applicable to other areas of China because it utilizes spatial data that are generally available.     

基于GIS的土壤全氮空间分布估算——以江西省兴国县为例

运用GIS的空间分析技术和DEM,在区域范围内,可以表征基于母岩和地形因子的土壤-景观模型。本次研究根据兴国县151个样点数据,分析TN和地形因子的相关关系,建立回归模型,进行估算。结果表明:表层土壤中TN含量平均值为1.06g/kg,千枚岩发育的土壤中TN的平均含量最高,为1.35g/kg ;砂页岩发育的土壤中TN的平均含量最低,为0.88g/kg 。空间分布上:TN含量在0.5g/kg~1.0g/kg的面积最大,为1580km²,TN含量在2.0g/kg以上的面积最小,为127km²。地形变量中坡向对TN含量影响最大,TN含量和母岩、海拔、坡向存在着正相关关系,坡度和TN含量的相关关系不明显。利用回归分析模型和DEM(30m×30m),估算TN的空间分布,R²为0.637。     

基于分层策略和模糊推理的北方石质山区土壤厚度制图——以滦平县虎什哈流域为例

土壤厚度信息对土壤碳储量估算、水文-生态过程模拟等有着重要影响,但我国北方石质山区缺乏现势性好、分辨率高的土壤厚度分布数据及其快速获取方法。基于北方石质山区土壤厚度的分布特点,先将地貌信息和植被类型叠加生成制图单元,再根据每类单元的特征,针对性地选取地形参数和地表覆被参数作为指示土壤厚度空间分布的环境变量,结合少量土壤样点,依据模糊推理模型,构建了基于分层制图策略的土壤厚度空间推测方法。以河北省滦平县虎什哈流域为例,利用从ASTER GDEMs提取的地形变量和从我国环境减灾卫星影像(HJ-A)提取的地表覆被信息对土壤厚度进行了推测。结果表明,该方法精度较高,是利用免费地形和遥感数据进行低成本快速土壤制图的实用途径。     

基于定性和定量辅助变量的土壤有机质空间分布预测——以四川三台县为例

准确获取土壤性质的空间分布信息,是区域土壤资源优化利用和土壤环境保护的需要。以川中丘陵区三台县为案例区,运用人工神经网络模型,构建融合区域定性及定量辅助变量的空间预测方法,模拟三台县土壤有机质的空间分布格局。结果表明,研究区土壤有机质在4.20~47.60 g kg^-1之间,平均为17.97 g kg^-1;变异系数为36.89%,属中等程度变异。土壤有机质的块金值与基台值之比为0.742,变程为7.0 km,即空间自相关性较弱。不同土壤类型间有机质含量差异显著;土属的空间分布较土类能更好地揭示研究区土壤有机质含量空间分布格局的差异。除土壤类型因素的影响外,坡度、地形湿度及植被盖度是研究区土壤有机质空间变异的主要因子。融合土壤类型因素和地形植被因子的神经网络模型预测结果,比普通克里格法、回归克里格法以及神经网络结合普通克里格的方法,更符合研究区地学规律和实际情况;其预测结果的平均绝对误差、平均相对误差和均方根误差较其他3种方法均降低幅度显著。同时,该方法对极值有较好的预测能力。研究为复杂环境条件下准确获取区域土壤性质的空间分布信息提供了较可行的方法。     

地形因子对京西门头沟区土地利用/覆盖变化的影响

以土地利用数据和数字高程模型(DEM)为基础,运用GIS对空间数据和属性数据的管理和分析功能,用高程、坡度、坡向3个地形因子分析了1990年与2005年北京门头沟地区的土地覆盖现状以及土地利用动态变化度。结果表明,地形因子对土地利用类型的分布规律有明显的影响,研究区在最近的15 a农业用地减少,城镇建设用地大幅度增加,社会经济和政治因素对土地利用/覆盖动态变化的驱动作用显著。     

Spatial variability of soil moisture content and its relation to environmental indices in a semi-arid gully catchment of the Loess Plateau, China

The degree of spatial variability of soil moisture and the ability of environmental attributes to predict that variability were studied at the Da Nangou catchment (3·5 km²) in the semi-arid loess area of China. Soil moisture measurements were performed biweekly at five depths in the soil profile (0–5 cm, 10–15 cm, 20–25 cm, 40–45 cm and 70–75 cm) from May to October 1998 and from May to September 1999 using Delta-T theta probe. Results indicated that with increasing soil depth, the mean soil moisture content increases significantly for five layers and the coefficients of variation (CV) also increases with depth from 10–15 cm. It was observed that heavier rains and higher mean moisture contents are often associated with lower spatial variability (CV). Environmental attributes such as land use and topography play controlling roles in the spatial distribution of soil moisture content. However, the relative roles of these environmental indices vary with soil depth. The dominant controls on spatial variability of the time-averaged soil moisture changes from land use, aspect, relative elevation and hillslope position in the surface soil (0–5 cm) to relative elevation, hillslope position and aspect in the subsurface soil (10–15 cm, 20–25 cm), and to land use, relative elevation and slope gradient at larger depths (40–45 cm, 70–75 cm). The dynamic behavior of influences of different environmental indices on the layer-averaged soil moisture depends on several factors. In general, the correlation of soil moisture with slope gradient shows a more significant increase following a greater amount of antecedent precipitation (except for the extremely heavy storms), and declines afterwards. The relation of soil moisture with relative elevation and hillslope position exhibits an opposite trend. It was observed that the influence of land use corresponds to the difference in vegetative characteristics, with a stronger influence in June and August with a greater difference in vegetation. A significant influence of cos(aspect) was found during early spring and autumn with a rapid transient in solar irradiation. Finally, it was found that the sample size is adequate to estimate the catchment mean soil moisture at all five depths and on all 10 observations in 1999 (81 sites), while it is only enough for the upper soil layers (0–5 cm and 10–15 cm) in 1998 (26 sites).     

Vertical distribution of soil moisture and surface sandy soil wind erosion for different types of sand dune on the southeastern margin of the Mu Us Sandy Land, China

Soil moisture is a critical state affecting a variety of land surface and subsurface processes. We report investigation results of the factors controlling vertical variation of soil moisture and sand transport rate of three types of dunes on the southeastern margin of the Mu Us Sandy Land. Samples were taken from holes drilled to a depth of 4 m at different topographic sites on the dunes, and were analyzed for soil moisture, grain-size distribution and surface sediment discharge. The results show that: (1) The average soil moisture varies in different types of dunes, with the following sequences ordered from highest to lowest: in the shrubs-covered dunes and the trees-covered dunes the sequence is from inter-dunes lowland to windward slope to leeward slope. The average moisture in the bare-migratory sand dunes is sequenced from inter-dunes lowland to leeward slope to windward slope. (2) Vegetation form and surface coverage affect the range of soil moisture of different types of dunes in the same topographic position. The coefficient of variation of soil moisture for shrubs-covered dunes is higher than that of other types of dune. (3) The effect of shrubs on dune soil moisture is explained in terms of the greater ability of shrubs to trap fine-grained atmospheric dust and hold moisture. (4) The estimated sand transport rates over sand dunes with sparse shrubs are less than those over bare-migratory dunes or sand dunes with sparse trees, indicating that shrubs are more effective in inhibiting wind erosion in the sandy land area.     

Sediment yield variability in Spain: a quantitative and semiqualitative analysis using reservoir sedimentation rates

An existing dataset of area-specific sediment yield (SSY) for 60 catchments in Spain that was retrieved from sediment deposition rates in reservoirs [Avendaño Salas, C., Sanz Montero, E., Rayán, C., Gómez Montaña, 1997. Sediment yield at Spanish reservoirs and its relationship with the drainage basin area. In: Proceedings of the 19th Symposium of Large Dams, Florence, 1997. ICOLD (International Committee on Large Dams), pp. 863–874] reveals that catchment area alone explains only 17% of the variability in SSY. In this study, an attempt to explain the remaining variability in SSY was made using a quantitative and a semiqualitative approach for 22 catchments. During a field survey, the 22 selected catchments were characterised by topography, vegetation cover, lithology, shape and the presence of gullies in the broad vicinity of the reservoir. This information was used to develop a factorial scoring index model that provides a fairly accurate and reliable prediction of SSY. A classical multiple regression model using climatic, topographic and land use properties derived from regional datasets could not explain as much variance as the qualitative index model, nor did it appear to be as reliable. The same conclusion could be drawn when using the CORINE soil erosion risk map of southern Europe. The low prediction capability of the multiple regression models and the CORINE soil erosion risk map could be attributed mainly to the fact that these methods do not incorporate gully erosion and that the land cover data are not a good representation of soil cover. Both variables have been shown to be of great importance during the field surveys. Future assessments of SSY could be quickly and efficiently made using the proposed factorial scoring index model. In comparison with other models, which demand more data, the index model offers an alternative prediction tool.     

松嫩高平原县域土地利用空间格局地形梯度特征分析——以巴彦县为例

以黑龙江省巴彦县为研究区,基于研究区DEM数据,运用土地利用类型的分布指数、土地利用结构的信息熵、均衡度和优势度,明确研究区土地利用空间格局的地形梯度特征及地形因子对土地利用空间格局的影响。结果表明：① 研究区土地利用空间格局在地形因子的作用下存在明显的梯度特征,旱地、水田、草地、水域、建设用地及其他用地主要分布在较低地形级别区,林地主要分布在较高地形级别区。② 除林地以外,其他土地利用类型分布的优势地形位均位于较低地形级别区,林地分布的优势地形位位于较高地形级别区,各土地利用类型分布的优势区域不尽相同。③ 研究区土地利用结构的信息熵及均衡度变化趋势相同,优势度的变化趋势与信息熵、均衡度的变化趋势相反,土地利用空间格局与地形因子具有显著的相关性。     

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.     

岳西县土壤侵蚀空间分布与环境因子的关系

以岳西县2004年TM影像为数据源,通过目视解译的方法,得到2004年土壤侵蚀强度矢量图。利用Arc-GIS软件,将土壤侵蚀图与不同的环境因子图进行叠加分析,探讨了岳西县2004年土壤侵蚀空间分布与环境因子的关系。结果表明:从土壤侵蚀与土壤类型的关系看,土壤侵蚀主要发生在麻石黄棕壤、黄棕壤性麻石土及麻石棕壤上。从不同土地利用方式看,侵蚀土壤主要发生在有林地、灌木林、水田、高覆盖草地地区。从土壤侵蚀与坡度的关系看,土壤侵蚀主要发生在3°～25°的坡度上,尤其在8°～25°坡度上居多。从土壤侵蚀与坡向的关系看,山地的北坡、西南坡、西坡及西北坡容易发生土壤侵蚀。从土壤侵蚀与海拔的关系看,200～1000 m地区土壤侵蚀比较显著,尤其500～1000 m的地区土壤侵蚀发生量多。     

Remote-sensing data reveals the response of soil erosion intensity to land use change in Loess Plateau,China

Developing an effective approach to rapidly assess the effects of restoration projects on soil erosion intensity and their extensive spatial and temporal dynamics is important for regional ecosystem management and the development of soil conservation strategies in the future. This study applied a model that was developed at the pixel scale using water soil erosion indicators (land use, vegetation coverage and slope) to assess the soil erosion intensity in the Loess Plateau, China. Landsat TM/ETM+ images in 2000, 2005 and 2010 were used to produce land use maps based on the object-oriented classification method. The MODIS product MOD13Q1 was adopted to derive the vegetation coverage maps. The slope gradient maps were calculated based on data from the digital elevation model. The area of water soil-eroded land was classified into six grades by integrating slope gradients, land use and vegetation coverage. Results show that the Grain-To-Green Project in the Loess Plateau worked based on the land use changes from 2000 to 2010 and enhanced vegetation restoration and ecological conservation. These projects effectively prevented soil erosion. During this period, lands with moderate, severe, more severe and extremely severe soil erosion intensities significantly decreased and changed into less severe levels, respectively. Lands with slight and light soil erosion intensities increased. However, the total soil-eroded area in the Loess Plateau was reduced. The contributions of the seven provinces to the total soil-eroded area in the Loess Plateau and the composition of the soil erosion intensity level in each province are different. Lands with severe, more severe and extremely severe soil erosion intensities are mainly distributed in Qinghai, Ningxia, Gansu and Inner Mongolia. These areas, although relatively small, must be prioritised and preferentially treated.     

滇东北山区坡耕地水土流失与可持续利用图的基本内容与编制方法

坡耕地水土流失与可持续利用图是综合表示滇东北山区各个坡耕地单元的水土流失状况及其可持续利用的专题地图。该图表示的基本内容是坡耕地类型、土壤类型、坡度级别、侵蚀强度、可持续利用程度、合理利用方向与措施。以土壤流失方程为基础,应用地理信息系统编制了该图。该图的编制为治理本区坡耕地水土流失、合理利用坡耕地资源提供了科学依据。     

Land use and climate control the spatial distribution of soil types in the grasslands of Inner Mongolia

The spatial distribution of soil types is controlled by a set of environmental factors such as climate, organisms, parent material and topography as well as time and space. A change of these factors will lead to a change in the spatial distribution of soil types. In this study, we use a digital soil mapping approach to improve our knowledge about major soil type distributing factors in the steppe regions of Inner Mongolia (China) which currently undergo tremendous environmental change, e.g. climate and land use change. We use Random Forests in an effort to map Reference Soil Groups according to the World Reference Base for Soil Resources (WRB) in the Xilin River catchment. We benefit from the superior prediction capabilities of RF and additional interpretive results in order to identify the major environmental factors that control spatial patterns of soil types. The nine WRB soil groups that were identified and spatially predicted for the study area are Arenosol, Calcisol, Cambisol, Chernozem, Cryosol, Gleysol, Kastanozem, Phaeozem and Regosol.Model and prediction performances of the RF model are high with an Out-of-Bag error of 51.6% for the model and a misclassification error for the predicted map of 28.9%. The main controlling factors of soil type distribution are land use, a set of topographic variables, geology and climate. However, land use and climate are of major importance and topography and geology are of minor importance. The visualizations of the predictions, the variable importance measures as result of RF and the comparisons of these with the spatial distribution of the environmental factors delivered additional, quantitative information of these controlling factors and revealed that intensively grazed areas are subjected to soil degradation. However, most of the area is still governed by natural soil forming processes which are driven by climate, topography and geology. Most importantly though, our study revealed that a shift towards warmer temperatures and lower precipitation regimes will lead to a change of the spatial distribution of RSGs towards steppe soils that store less carbon, i.e. a decrease of spatial extent of Phaeozems and an increase of spatial extent of Chernozems and Kastanozems.     

SCS-CN 径流模型中CN值确定方法研究

 径流计算是区域径流量预报和水土资源评价模型中的重要组成部分。SCS-CN径流模型是一种常用的径流计算方法。径流曲线数（CN）是SCS-CN模型中反映降雨前流域特征的一个综合参数。用北京密云石匣3个小区实测降雨径流资料,用平均值法、中值法、算术平均值法、S对数频率分布法以及渐近线法来反推CN值,并用反推的CN值计算了径流深。用模型效率系数、相关系数和合格率比较了这5种方法确定的CN值计算径流深的优劣。结果表明：从模型有效系数来看,渐近线法得到的模型有效系数最好;从相关系数和合格率来看,算术平均值法的结果最好。相对于渐近线法来讲,算术平均值法计算CN简单,因此建议在计算CN值时用算术平均值法。     

土地覆被类型空间格局与地形因子的定量关联及其应用

以全国1∶25万土地覆被数据、全国公里网格DEM数据和县级行政区划数据为基础,首先,在国家尺度上宏观分析地形因子对土地覆被类型空间分布格局的影响,土地覆被类型空间分布宏观格局受海拔高度、坡度、地表起伏度的影响明显,而与坡向的关系并不显著;其次,以县级行政单元为统计分析样本,利用多元线性回归分析方法,在国家和区域两个尺度建立土地覆被类型面积占比与主要地形因子之间的定量模型,在6类土地覆被类型中,除草地以外,其他5类土地覆被类型的面积占比均与地形因子之间呈显著相关关系,显著性由高到低的排列顺序依次为森林、农田、荒漠、聚落和湿地水体;最后,以相关性最好的森林的空间分布为例,说明了将所建立的模型应用于栅格单元上某种土地覆被类型面积占比估算的可能性,虽然估算结果与实际情况存在差异,但总体趋势基本保持一致,特别是土地覆被类型面积占比大的区域。     

Comprehensive Evaluation of the Suitability of Agricultural Land in Myanmar

Myanmar is a country with an economy based on agriculture. It has rich agricultural resources and great potential for development. The development of agriculture in Myanmar is becoming increasingly important to international food security. Assessments of agricultural land resources in Myanmar are the basis for the country’s agricultural development and for food security evaluations. In this paper we used the MaxEnt model to analyze the relationship between the suitability of land for agricultural reclamation and the main environmental variables in Myanmar, and then constructed a model to comprehensively evaluate the suitability of land for agriculture in Myanmar. The results show that: 1) the overall accuracy of the MaxEnt model is high (AUC>0.8), which means there is a high correlation between the database of selected environmental indicators and the true distribution of cultivated land in Myanmar. 2) Soil depth is the most important factor affecting the suitability of land for agriculture in Myanmar. When the thickness of soil layer is less than 100 cm, the suitability of land for agriculture is low. With respect to topographic conditions, slope is the main factor affecting suitability. When the slope is greater than 20 degrees, the suitability of land for agriculture is low. With respect to climate conditions, precipitation is the main influencing factor. There is a positive correlation between river network density and land suitability. 3) Currently, 400 000 km² of the land resources in Myanmar are suitable for agriculture, and of this amount 290 000 km² are highly suitable, accounting for nearly 40% of the country's land area. The highly suitable land is distributed mainly in Magway, Sagaing, Ayeyarwady and Yangon provinces. The provinces are also important grain production areas in Myanmar, and this serves to validate the effectiveness of the method used in this paper.     

Assessing artificial neural networks coupled with wavelet analysis for multi-layer soil moisture dynamics prediction

Soil moisture simulation and prediction in semi-arid regions are important for agricultural production,soil conservation and climate change.However,considerable heterogeneity in the spatial distribution of soil moisture,and poor ability of distributed hydrological models to estimate it,severely impact the use of soil moisture models in research and practical applications.In this study,a newly-developed technique of coupled(WA-ANN) wavelet analysis(WA) and artificial neural network(ANN) was applied for a multi-layer soil moisture simulation in the Pailugou catchment of the Qilian Mountains,Gansu Province, China.Datasets included seven meteorological factors:air and land surface temperatures,relative humidity,global radiation, atmospheric pressure,wind speed,precipitation,and soil water content at 20,40,60,80,120 and 160 cm.To investigate the effectiveness of WA-ANN,ANN was applied by itself to conduct a comparison.Three main findings of this study were:(1) ANN and WA-ANN provided a statistically reliable and robust prediction of soil moisture in both the root zone and deepest soil layer studied(NSE >0.85,NSE means Nash-Sutcliffe Efficiency coefficient);(2) when input meteorological factors were transformed using maximum signal to noise ratio(SNR) and one-dimensional auto de-noising algorithm(heursure) in WA, the coupling technique improved the performance of ANN especially for soil moisture at 160 cm depth;(3) the results of multi-layer soil moisture prediction indicated that there may be different sources of water at different soil layers,and this can be used as an indicator of the maximum impact depth of meteorological factors on the soil water content at this study site.We conclude that our results show that appropriate simulation methodology can provide optimal simulation with a minimum distortion of the raw-time series;the new method used here is applicable to soil sciences and management applications.